Here, we run a variety of sensitivity analyses on the original and time-averaged networks. This code has been adapted from Peeples, Matthew A. 2017. Network Science and Statistical Techniques for Dealing with Uncertainties in Archaeological Datasets. [online]. Available: www.mattpeeples.net/netstats.html. Here, we use igraph rather than the sna and network packages.
Currently, most of this RMD focuses only on 1 Chaco dataset (i.e., AD 800). This script now fits into the workflow with the rest of the Chaco scripts. However, this script still needs to be updated to apply to all of the networks rather than just the 1 subset (i.e., the original AD 800 network).
# First, load in all of the the igraph objects (i.e., Chaco_original_graphs and Chaco_ta_graphs). However, we only need the second one, as these will include the original graphs too.
load("../Data/Chaco_graph_objects.RData")
# The lists are unnested for 1 level.
Chaco.nets <- purrr::map(Chaco_ta_graphs, 1)
# Give names to each list of time period to keep track.
chaco.names <- c("chaco800", "chaco825", "chaco850", "chaco875",
"chaco900", "chaco925", "chaco950", "chaco975",
"chaco1000", "chaco1025", "chaco1050", "chaco1075",
"chaco1100", "chaco1125", "chaco1150", "chaco1175",
"chaco1200", "chaco1225", "chaco1250", "chaco1275")
names(Chaco.nets) <- chaco.names
#I will simply get 1 original network that was created in 'Chaco_time_average.R', and use that to build the basis for the analysis.
tn <- Chaco_original_graphs[[1]]
#tn <- Chaco_ta_graphs[[1]][[1]][[1]]
## `summarise()` has grouped output by 'orig.net', 'num.net'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'orig.net', 'num.net'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'orig.net', 'num.net'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'orig.net', 'num.net'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'orig.net', 'num.net'. You can override using the `.groups` argument.
Here, we determine whether the most central node is consistently the most central.